50 Ways to Leak Your Data: An Exploration of Apps’ Circumvention of the Android Permissions System

Title50 Ways to Leak Your Data: An Exploration of Apps’ Circumvention of the Android Permissions System
Publication TypeConference Paper
Year of Publication2019
AuthorsReardon, J., Feal Á., Wijesekera P., Bar On A. Elazari, Vallina-Rodriguez N., & Egelman S.
Published inProceedings of the 24th USENIX Security Symposium
Abstract

Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy.

In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus.

Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work.

Acknowledgment

This work was supported by the U.S. National Security Agency’s Science of Security program (contract H98230-18- D-0006), the Department of Homeland Security (contract FA8750-18-2-0096), the National Science Foundation (grants CNS-1817248 and grant CNS-1564329), the Rose Foundation, the European Union’s Horizon 2020 Innovation Action program (grant Agreement No. 786741, SMOOTH Project), the Data Transparency Lab, and the Center for Long-Term Cybersecurity at U.C. Berkeley. The authors would like to thank John Aycock, Irwin Reyes, Greg Hagen, René Mayrhofer, Giles Hogben, and Refjohürs Lykkewe.

ICSI Research Group

Usable Security and Privacy